Sustainability
Introduction
We all have a role to play in creating a sustainable future for ourselves and for generations to come. One of the most pressing issues we face today is the high level of energy consumption and the resulting impact on the environment. In 2021, the United States alone consumed a staggering 3,930 terawatt hours of energy, with a significant portion of it coming from non-renewable sources like fossil fuels. This has a detrimental effect on the environment, contributing to climate change, air and water pollution, and other environmental issues. Melting glaciers are threatening to flood our valuable land and cities. An estimated 15 million people around the world are at risk of suffering sudden, deadly, and destructive flooding events due to the warming climate. By caring about sustainability and making conscious choices about energy use, we can help to reduce our carbon footprint and create a more sustainable future.
At Amherst College, we have a responsibility to reduce our energy consumption and minimize our environmental impact. By reducing energy consumption, we can save money and preserve resources for future generations. In our analysis, we look at sustainability within a few freshman dorms at Amherst College. We then look at data across the world and compare energy consumption country-to-country. Our goal is to bring attention to just how much energy is consumed and consequently allow readers to analyze their own carbon footprints.
Website publication work flow
Edit Rmd
Knit to HTML to view progress. You may need to click “Open in Browser” for some content to show (sometimes content won’t show until you actually push your changes to GitHub and view the published website).
Commit and push changes when you are ready. The website may take a couple minutes to update automatically after the push, but you may need to clear your browser’s cache or view the page in a private/incognito window to see the changes more quickly.
Content
You can include text, code, and output as usual.
Remember to take full advantage of Markdown and follow our Style
Guide.
Examples and additional guidance are provided below.
Take note of the the default code chunk options in the
setup code chunk. For example, unlike the rest of the Rmd
files we worked in this semester, the default code chunk option is
echo = FALSE, so you will need to set
echo = TRUE for any code chunks you would like to display
in the blog. You should be thoughtful and intentional about the code you
choose to display.
Visualizations
Visualizations, particularly interactive ones, will be well-received. That said, do not overuse visualizations. You may be better off with one complicated but well-crafted visualization as opposed to many quick-and-dirty plots. Any plots should be well-thought-out, properly labeled, informative, and visually appealing.
If you want to include dynamic visualizations or tables, you should explore your options from packages that are built from htmlwidgets. These htmlwidgets-based packages offer ways to build lighterweight, dynamic visualizations or tables that don’t require an R server to run! A more complete list of packages is available on the linked website, but a short list includes:
- plotly: Interactive graphics with D3
- leaflet: Interactive maps with OpenStreetMap
- dygraphs: Interactive time series visualization
- visNetwork: Network graph visualization vis.js
- sparkline: Small inline charts
- threejs: Interactive 3D graphics
You may embed a published Shiny app in your blog if useful, but be aware that there is a limited window size for embedded objects, which tends to makes the user experience of the app worse relative to a dedicated Shiny app page. Additionally, Shiny apps will go idle after a few minutes and have to be reloaded by the user, which may also affect the user experience.
Any Shiny apps embedded in your blog should be accompanied by the link to the published Shiny app (I did this using a figure caption in the code chunk below, but you don’t have to incorporate the link in this way).
Tables
DT package
The DT package is great for making dynamic tables that can be displayed, searched, and filtered by the user without needing an R server or Shiny app!
Note: you should load any packages you use in the setup
code chunk as usual. The library() functions are shown
below just for demonstration.
library(DT)
mtcars %>%
select(mpg, cyl, hp) %>%
datatable(colnames = c("MPG", "Number of cylinders", "Horsepower"),
filter = 'top',
options = list(pageLength = 10, autoWidth = TRUE))kableExtra package
You can also use kableExtra for customizing HTML tables.
library(kableExtra)
summary(cars) %>%
kbl(col.names = c("Speed", "Distance"),
row.names = FALSE) %>%
kable_styling(bootstrap_options = "striped",
full_width = FALSE) %>%
row_spec(0, bold = TRUE) %>%
column_spec(1:2, width = "1.5in") | Speed | Distance |
|---|---|
| Min. : 4.0 | Min. : 2.00 |
| 1st Qu.:12.0 | 1st Qu.: 26.00 |
| Median :15.0 | Median : 36.00 |
| Mean :15.4 | Mean : 42.98 |
| 3rd Qu.:19.0 | 3rd Qu.: 56.00 |
| Max. :25.0 | Max. :120.00 |
Images
Images and gifs can be displayed using code chunks:
“Safe Space” by artist Kenesha Sneed
This is a figure caption
You may also use Markdown syntax for displaying images as shown below, but code chunks offer easier customization of the image size and alignment.
